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The hypoexponential is a series of k exponential distributions each with their own rate , the rate of the exponential distribution. If we have k independently distributed exponential random variables X i {\displaystyle {\boldsymbol {X}}_{i}} , then the random variable,
Hypoexponential distribution – 2 or more phases in sequence, can be non-identical or a mixture of identical and non-identical phases, generalises the Erlang. As the phase-type distribution is dense in the field of all positive-valued distributions, we can represent any positive valued distribution.
In probability theory and statistics, the exponential distribution or negative exponential distribution is the probability distribution of the distance between events in a Poisson point process, i.e., a process in which events occur continuously and independently at a constant average rate; the distance parameter could be any meaningful mono-dimensional measure of the process, such as time ...
It is named the hyperexponential distribution since its coefficient of variation is greater than that of the exponential distribution, whose coefficient of variation is 1, and the hypoexponential distribution, which has a coefficient of variation smaller than one.
Linnik distribution – redirects to Geometric stable distribution; LISREL – proprietary statistical software package; List of basic statistics topics – redirects to Outline of statistics; List of convolutions of probability distributions; List of graphical methods; List of information graphics software; List of probability topics
In probability theory, one definition of a subexponential distribution is as a probability distribution whose tails decay at an exponential rate, or faster: a real-valued distribution is called subexponential if, for a random variable ,
The probability distribution of the sum of two or more independent random variables is the convolution of their individual distributions. The term is motivated by the fact that the probability mass function or probability density function of a sum of independent random variables is the convolution of their corresponding probability mass functions or probability density functions respectively.
Special cases of distributions where the scale parameter equals unity may be called "standard" under certain conditions. For example, if the location parameter equals zero and the scale parameter equals one, the normal distribution is known as the standard normal distribution, and the Cauchy distribution as the standard Cauchy distribution.